LightweightCohere

Command R7B

Command R7B is Cohere's lightweight model in the Command family, offering 128K context window and tool calling for efficient text generation tasks.

Context 128K
Tier Lightweight
Tools Supported
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API Pricing

No pricing data available for this model at the moment.

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
Cohere
Family
Command
Tier
Lightweight
Context Window
128K
Modalities
Text

Capabilities

Tool Calling
Yes
Open Source
No
Subtypes
Chat Completion

Strengths & Limitations

  • 128K token context window supports long document processing
  • Tool calling capability enables API integrations and structured outputs
  • 7B parameter size provides faster inference than larger models
  • Part of Cohere's established Command model family
  • Designed for cost-effective deployment at scale
  • Chat completion format supports conversational applications
  • Proprietary model with no open-source weights available
  • Text-only modality lacks image or multimodal input support
  • Smaller parameter count limits complex reasoning compared to flagship models
  • Lightweight tier may have reduced capabilities versus Command R+ or other larger variants

Key Features

•128K token context window
•Tool calling with external API integration
•Chat completion interface
•Text-only input and output
•Streaming response support
•7 billion parameter architecture
•RESTful API access

About Command R7B

Command R7B is Cohere's lightweight model in the Command family, positioned as an efficient alternative to larger models in the lineup. As a 7 billion parameter model, it sits in the smaller tier of Cohere's offerings, designed for scenarios where speed and cost-effectiveness are priorities over maximum capability. The model supports a 128K token context window and includes tool calling functionality, enabling integration with external APIs and structured workflows. Command R7B handles text-only inputs and outputs, focusing on chat completion tasks. The model is proprietary and accessed through Cohere's API rather than being available for local deployment. Command R7B targets use cases where organizations need reliable language model capabilities without the computational overhead of frontier models. It competes with other lightweight options in the 7B parameter range, offering Cohere's approach to balancing performance with efficiency for high-volume applications.

Common Use Cases

Command R7B is well-suited for applications requiring efficient text processing with moderate complexity, such as customer service chatbots, content moderation, text classification, and document summarization where the 128K context window handles longer inputs. The tool calling capability makes it appropriate for workflow automation, API integration tasks, and structured data extraction. Organizations with high-volume use cases benefit from its lightweight architecture, while the extended context window supports applications like long-form content analysis, technical documentation processing, and multi-turn conversations that need to reference extensive conversation history.

Frequently Asked Questions

How much does Command R7B cost per million tokens?

Command R7B pricing varies by provider and may include different rates for input and output tokens. Check the pricing table above for current rates across all available providers offering this model.

What is Command R7B best used for?

Command R7B excels at high-volume text processing tasks like customer support, content moderation, and document analysis where its 128K context window and tool calling capabilities provide good functionality while maintaining cost efficiency. It's ideal when you need reliable performance without the overhead of larger models.

How does Command R7B compare to other 7B parameter models?

Command R7B offers a notably large 128K token context window compared to many 7B models, plus built-in tool calling functionality. However, it's proprietary rather than open-source, and text-only compared to some competitors that offer multimodal capabilities.